AI ML Engineering Jobs at Google with Visa Sponsorship
AI ML Engineering jobs at Google span research, product, and infrastructure teams, tackling large-scale models, ML infrastructure, and applied AI systems. The company has a consistent track record of sponsoring work visas for this function, making it a realistic target if you're on H-1B visa, H-1B1 visa, or E-3 visa status.
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INTRODUCTION
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. As a part of the GenAI for Youth, a team within Kids and Families, you will be dedicated to developing the next-generation of scalable, systemically responsible, and high-integrity AI frameworks that power experiences across Google's products. Your mission is to bridge the gap between foundational model research and highly calibrated, age-appropriate production environments. You will build unified infrastructure that solves complex engineering, user classification, and behavioral modeling challenges once for the entire ecosystem. You will operate across multi-site engineering hubs, establish the technical strategy, algorithmic steering frameworks, and automated validation layers necessary to ensure that Google's AI-driven platform surfaces scale with complete structural accuracy, performance safety, and long-term ecosystem health. The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
ROLE AND RESPONSIBILITIES
Learn more about benefits at Google.
- Develop shared algorithmic frameworks and foundational math in core modeling domains, including runtime behavior steering, latent-space pattern detection, optimization loops, and fine-tuning rooted in industry best practices.
- Streamline engineering efforts across global product lines by shifting fragmented, application-specific pipelines into cohesive, highly scalable, and reusable platform layers.
- Architect definitive, automated evaluation methodologies and standardized benchmarking metrics to provide scientifically rigorous validation models, eliminating manual execution overhead.
- Serve as a technical conduit between product, data science, applied engineering groups, machine learning research organizations, and centralized core infrastructure teams.
- Act as a strategic advisory partner to executive engineering leadership on platform maturity, long-term technical debt reduction, and operational capacity across technical workstreams.
BASIC QUALIFICATIONS
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with software development, system design, or machine learning infrastructure.
- 8 years of experience working with trust and safety, classification problems, large language model (LLM) safety architectures, fraud or risk detection, identity verification, or adversarial machine learning.
PREFERRED QUALIFICATIONS
- Familiarity with industrial-scale data pipelines, internal data orchestration engines, and high-performance compute infrastructure.
- Demonstrated ability to lead by influence and drive technical alignment across sprawling, matrixed engineering groups without direct operational authority.
- Exceptional systems-thinking capabilities, with a documented history of transforming fragmented technical processes into highly reusable, automated platform engines.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
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Get Access To All JobsTips for Finding AI ML Engineering Jobs at Google
Align your portfolio to Google's AI research areas
Google prioritizes ML engineers with experience in large language models, distributed training infrastructure, or ML compilers. Tailor your GitHub, publications, or project work to these domains before applying, since hiring committees assess technical fit against active research priorities.
Target teams that file LCAs year-round
Google's AI and Research org files Labor Condition Applications with DOL continuously, not just during H-1B cap season. Roles under Google DeepMind, Google Research, and core ML infrastructure teams are more likely to have active sponsorship pipelines outside of April.
Use Migrate Mate to filter open roles by visa type
Google posts AI ML Engineering roles across multiple teams simultaneously, and not every opening is tied to a sponsoring team. Use Migrate Mate to filter specifically for roles where H-1B, H-1B1 visa, or E-3 sponsorship is confirmed.
Negotiate offer timing around your OPT or grace period
If your OPT expires before October 1, ask your Google recruiter about cap-gap coverage or a STEM OPT extension. USCIS's cap-gap rule lets you keep working if your H-1B petition is filed before OPT ends, but the timing has to be deliberate.
Prepare for Google's structured technical interview format
Google uses a standardized interview loop for ML roles: coding rounds, ML system design, and a research depth interview. Prepare ML system design answers that reference real production constraints, not just academic benchmarks, since interviewers expect applied scale thinking.
Clarify E-3 or H-1B1 eligibility early with your recruiter
E-3 and H-1B1 visas sit outside the annual H-1B lottery cap and can be processed faster. If you're an Australian or Singapore or Chile national, confirm with Google's immigration team that your target role qualifies as a specialty occupation under the correct visa category.
Frequently Asked Questions
Does Google sponsor H-1B visas for AI ML Engineers?
Yes, Google sponsors H-1B visas for AI ML Engineering roles. Google also sponsors H-1B1 visas for nationals of Singapore and Chile, and E-3 visas for Australian nationals. Sponsorship is handled through Google's internal immigration team in coordination with outside counsel, and the process typically begins after an offer is extended.
Which visa types does Google commonly use for AI ML Engineering roles?
Google sponsors H-1B, H-1B1 visa, and E-3 visas for AI ML Engineering positions. H-1B is subject to the annual lottery cap, with registration in March and an October 1 start date. H-1B1 and E-3 are cap-exempt and can be filed at any point in the year, which makes them faster options for eligible nationals.
What qualifications does Google expect for AI ML Engineering roles?
Google generally expects a bachelor's degree in computer science, electrical engineering, or a related field, with a master's or PhD preferred for research-adjacent roles. Practical experience with ML frameworks like JAX, TensorFlow, or PyTorch is expected. Roles on Google DeepMind or Google Research teams often require demonstrated publication history or equivalent industry research output.
How do I apply for AI ML Engineering jobs at Google?
Applications go through Google's careers portal at careers.google.com. Roles are posted by team, so filtering by function and location matters. If you need visa sponsorship, Migrate Mate lets you browse open AI ML Engineering positions at Google filtered by visa type, so you can focus on roles where sponsorship is confirmed before you apply.
How do I understand the H-1B filing timeline if I get an offer from Google?
If you're subject to the H-1B cap, Google must register you in USCIS's annual lottery, which opens in March. Selection is random, and if chosen, the petition is filed by June for an October 1 start date. If you're already in H-1B status with another employer, Google can file a transfer petition outside cap season with no lottery required.